Following the evaluation setting of Chen et al. [1]}, the baseline methods for comparison are divided into three main categories: UDA, SSL, and SSDA, where we add additional state-of-the-art methods to our comparison. UDA uses fully labeled source domain and unlabeled target domain data for training; SSL uses a small amount of labeled target and the remaining unlabeled target data for training; SSDA uses fully labeled source data, a small amount of labeled target data as well as reminding unlabeled target data to train the model. The supervised approach uses all available (source and target) labeled data without attempting to utilize unlabeled target data. All the trained models are evaluated on the same validation set. We evaluate two variants of our proposed approach differing by the way intra-domain adaptation is performed: Ours\(^{1}\)  uses ClassMix [2]} whereas Ours\(^{2}\)  uses ComplexMix [3]}.
